Promoting Systematic Practices for Designing and Developing Edge Computing Applications via Middleware Abstractions and Performance Estimation

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Date
2021-04-09
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Publisher
Virginia Tech
Abstract

Mobile, IoT, and wearable devices have been transitioning from passive consumers to active generators of massive amounts of user-generated data. Edge-based processing eliminates network bottlenecks and improves data privacy. However, developing edge applications remains hard, with developers often have to employ ad-hoc software development practices to meet their requirements. By doing so, developers introduce low-level and hard-to-maintain code to the codebase, which is error-prone, expensive to maintain, and vulnerable in terms of security.

The thesis of this research is that modular middleware abstractions, exemplar use cases, and ML-based performance estimation can make the design and development of edge applications more systematic. To prove this thesis, this dissertation comprises of three research thrusts: (1) understand the characteristics of edge-based applications, in terms of their runtime, architecture, and performance; (2) provide exemplary use cases to support the development of edge-based application; (3) innovate in the realm of middleware to address the unique challenges of edge-based data transfer and processing. We provide programming support and performance estimation methodologies to help edge-based application developers improve their software development practices.

This dissertation is based on three conference papers, presented at MOBILESoft 2018, VTC 2020, and IEEE SMDS 2020.

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Keywords
Edge Computing, Programming Model, Data Sharing, Android, Performance Estimation, Machine learning
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